How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
links to conference publications in graph-based deep learning
The main features of naganandy/graph-based-deep-learning-literature are: Graph Representation Learning, Community Resources.
Open-source alternatives to naganandy/graph-based-deep-learning-literature include: dsgiitr/graph_nets — Graph Nets is a graph neural network library and educational toolkit implemented in PyTorch, providing implementations… shenweichen/graphembedding — GraphEmbedding is a graph network representation library and node embedding framework. It provides a toolkit for… dmlc/dgl — DGL is a Python library for building and training graph neural networks. It functions as a graph message passing… seongjunyun/graph_transformer_networks — Graph_Transformer_Networks is a graph neural network framework implemented in PyTorch for learning structural… npubird/knowledgegraphcourse — KnowledgeGraphCourse is a structured collection of graduate-level academic materials, lecture notes, and a… thunlp/openke — OpenKE is a knowledge graph embedding framework designed to transform structured knowledge graphs into low-dimensional…
Graph Nets is a graph neural network library and educational toolkit implemented in PyTorch, providing implementations of popular graph representation learning algorithms and research papers. The project covers core graph machine learning tasks including semi-supervised node classification, inductive and unsupervised node embedding generation, and neighborhood feature aggregation. The library supports diverse algorithmic approaches for processing network structures, ranging from shared-parameter graph convolutions and attention-weighted neighborhood aggregation to spectral Chebyshev filtering
KnowledgeGraphCourse is a structured collection of graduate-level academic materials, lecture notes, and a comprehensive curriculum focused on the theory and application of knowledge graphs. It serves as a markdown-based educational resource that provides navigable course modules and study guides. The material covers specialized research on integrating knowledge graphs with large language models to reduce hallucinations. It includes detailed guides on using the SPARQL language for storing large-scale graph datasets and executing optimized queries. The curriculum spans a broad range of capabi
DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks
GraphTransformerNetworks is a graph neural network framework implemented in PyTorch for learning structural representations and performing classification tasks on complex heterogeneous graphs and relational networks. The project provides automated preprocessing pipelines to transform raw graph datasets into standardized formats, alongside model training, forward passes, and gradient backpropagation executed through dynamic tensor operations. The architecture incorporates self-attention mechanisms applied directly to graph structures to learn contextual representations of nodes and edges ac